Mastering Java Machine Learning

Real-world case study

Here we present a case study that illustrates how to apply clustering and outlier techniques described in this chapter in the real world, using open-source Java frameworks and a well-known image dataset.

Tools and software

We will now introduce two new tools that were used in the experiments for this chapter: SMILE and Elki. SMILE features a Java API that was used to illustrate feature reduction using PCA, Random Projection, and IsoMap. Subsequently, the graphical interface of Elki was used to perform unsupervised learning—specifically, clustering and outlier detection. Elki comes with a rich set of algorithms for cluster analysis and outlier detection including a large number of model evaluators to choose from.

Note

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